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method based on in-depth analysis of near-wall physical phenomena” Applications are invited from candidates who meet the detailed requirements set out by the recruitment committee appointed in accordance
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. Expertise in modelling and simulation of coupled multi-physics-based nonlinear dynamical system through finite volume method approach is expected. Knowledge and fluency in English language. Highly motivated
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-ERA.NET project. The candidate is expected to grow and study multilayer magnetic films by means MBE, CVD, LEED, LEEM, and PEEM methods. Work at SOLARIS offers: • the opportunity to participate in the state
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of the obtained nanomaterials using methods including UV-Vis, FTIR, Raman and fluorescence spectroscopy, SEM/TEM, AFM, HPLC, DLS, and LDV. Investigation of release kinetics of active compounds under simulated and
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scientific English. 7. Knowledge and ability to operate computer software. 8. Analytical thinking, technical, and problem-solving skills. 9. Availability, desire for self-improvement, strong motivation
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of Task 2 (VEP and ERG measurements) and partially Task 1.3 (optoretinography measurements). In addition, he/she will oversee the work of the PhD student. She/he will: - develop effective methods
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action. Work Location(s) Number of offers available1Company/InstituteFaculty of Physics and Applied Computer ScienceCountryPolandCityKrakowPostal Code30-059StreetReymonta 19Geofield Contact State/Province
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knowledge of the details of the design of SO/STIX and ASO-S/HXI; • Excellent knowledge of the Python and IDL programming languages; • Knowledge of the statistical analysis methods; • Knowledge of English at a
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laboratory techniques: cell culture in vitro, western blot, cytometric analysis, and other molecular methods etc . • experience working with students LanguagesENGLISHLevelExcellent LanguagesPOLISHLevelGood
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journals and conference proceedings; experience in conducting research on image processing using deep neural networks; knowledge of methods of statistical data analysis; ability to work with large data sets